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We perform differential expression analysis of high-throughput sequencingcount data under a Bayesian nonparametric framework, removing sophisticatedad-hoc pre-processing steps commonly required in existing algorithms. Wepropose to use the gamma (beta) negative binomial process, which takes intoaccount different sequencing depths using sample-specific negative binomialprobability (dispersion) parameters, to detect differentially expressed genesby comparing the posterior distributions of gene-specific negative binomialdispersion (probability) parameters. These model parameters are inferred byborrowing statistical strength across both the genes and samples. Extensiveexperiments on both simulated and real-world RNA sequencing count data showthat the proposed differential expression analysis algorithms clearlyoutperform previously proposed ones in terms of the areas under both thereceiver operating characteristic and precision-recall curves.

BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data